A numerical investigation of a two-phase nanofluid flow with phase change materials in the thermal management of lithium batteries and use of machine learning in the optimization of the horizontal and vertical distances between batteries

Wang, Dan; Ali, Masood Ashraf; Alizadeh, As’ad; Chaturvedi, Rishabh; Ali, Mohamed R.; Sohail, Muhammad

doi:10.1016/j.csite.2022.102582

Cited by 10 publications

(1 citation statement)

References 83 publications

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“…The findings may have implications for improving the safety and reliability of battery systems. Wang et al [18] the research explores the application of a two-phase nanofluid flow with phase change materials to enhance the thermal performance of lithium batteries. Additionally, the paper incorporates machine learning techniques for optimizing horizontal and vertical distances between batteries.…”

Section: Introductionmentioning

confidence: 99%

Enhancing battery cell safety and lifespan through strategic application of insulating and phase change materials utilizing critical thickness for enhanced heat transfer

Kadam,

Gunjavate,

Bhise

2024

Eng. Res. Express

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Efficient and secure operation of electric vehicles relies significantly on the cooling system for lithium iron phosphate (LiFePO4) batteries, a key component in electric vehicle technology.. One of the critical challenges faced by electric vehicle is battery cooling to ensure optimal performance, extended battery life, and enhanced safety. The excessive heat generated during battery operation can lead to decrease in performance and potential safety hazards. Conventional cooling systems, such as air or liquid cooling, have limitations in terms of their cooling capacity, weight, and complexity. Therefore, there is a need to explore innovative cooling techniques that can effectively reduce the thermal issues associated with Electric Vehicles LiFePO4batteries. Employing a thermal insulating coating and phase change material at the critical thickness emerges as an innovative approach to mitigate the surface temperature of battery cells. This is evident during the charging phase, where the bare cell, Teflon-insulated, and paraffin wax-coated cells reached respective peak temperatures of 69 ˚C, 57 ˚C, and 53.3 ˚C. Notably, the Teflon-coated cell exhibited a 17.39% reduction in peak temperature compared to the bare cell, while the paraffin wax-coated cell displayed a more substantial 23.18% reduction. A similar temperature reduction trend is observed during the discharging phase of the battery cell. Utilizing insulating materials or phase change materials with a critical thickness significantly lowers surface temperatures, enhancing the safety of the battery cell and ensuring prolonged life.

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Section: Introductionmentioning

confidence: 99%

Enhancing battery cell safety and lifespan through strategic application of insulating and phase change materials utilizing critical thickness for enhanced heat transfer

Kadam,

Gunjavate,

Bhise

2024

Eng. Res. Express

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Solutions for Lithium Battery Materials Data Issues in Machine Learning: Overview and Future Outlook

Xue,

Qiu,

Peng

et al. 2024

Advanced Science

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The application of machine learning (ML) techniques in the lithium battery field is relatively new and holds great potential for discovering new materials, optimizing electrochemical processes, and predicting battery life. However, the accuracy of ML predictions is strongly dependent on the underlying data, while the data of lithium battery materials faces many challenges, such as the multi‐sources, heterogeneity, high‐dimensionality, and small‐sample size. Through the systematic review of the existing literatures, several effective strategies are proposed for data processing as follows: classification and extraction, screening and exploration, dimensionality reduction and generation, modeling and evaluation, and incorporation of domain knowledge, with the aim to enhance the data quality, model reliability, and interpretability. Furthermore, other possible strategies for addressing data quality such as database management techniques and data analysis methodologies are also emphasized. At last, an outlook of ML development for data processing methods is presented. These methodologies are not only applicable to the data of lithium battery materials, but also endow important reference significance to electrocatalysis, electrochemical corrosion, high‐entropy alloys, and other fields with similar data challenges.

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Computational investigation of double non-uniform magnetic field on heat transfer of nanofluid stream along 180-degree elbow pipe

Zhu,

Wang,

Hassanvand

et al. 2023

Case Studies in Thermal Engineering

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A numerical investigation of a two-phase nanofluid flow with phase change materials in the thermal management of lithium batteries and use of machine learning in the optimization of the horizontal and vertical distances between batteries

Cited by 10 publications

References 83 publications

Enhancing battery cell safety and lifespan through strategic application of insulating and phase change materials utilizing critical thickness for enhanced heat transfer

Enhancing battery cell safety and lifespan through strategic application of insulating and phase change materials utilizing critical thickness for enhanced heat transfer

Solutions for Lithium Battery Materials Data Issues in Machine Learning: Overview and Future Outlook

Computational investigation of double non-uniform magnetic field on heat transfer of nanofluid stream along 180-degree elbow pipe

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